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(v2.1.1.9098) update Py vigettes
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@ -49,6 +49,7 @@ if command -v Rscript > /dev/null; then
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if [ "$(Rscript -e 'cat(all(c('"'pkgload'"', '"'devtools'"', '"'dplyr'"') %in% rownames(installed.packages())))')" = "TRUE" ]; then
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Rscript -e "source('data-raw/_pre_commit_checks.R')"
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currentpkg=$(Rscript -e "cat(pkgload::pkg_name())")
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bash data-raw/AMRforRGPT.sh
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echo "- Adding changed files in ./data-raw and ./man to this commit"
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git add data-raw/*
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git add man/*
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@ -1,6 +1,6 @@
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Package: AMR
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Version: 2.1.1.9095
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Date: 2024-10-15
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Version: 2.1.1.9098
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Date: 2024-10-17
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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data analysis and to work with microbial and antimicrobial properties by
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2
NEWS.md
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NEWS.md
@ -1,4 +1,4 @@
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# AMR 2.1.1.9095
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# AMR 2.1.1.9098
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*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
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@ -50,14 +50,31 @@ from rpy2 import robjects
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from rpy2.robjects import pandas2ri
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from rpy2.robjects.packages import importr, isinstalled
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import pandas as pd
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# import importlib.metadata as metadata
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# Check if the R package is installed
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# Check if AMR package is installed in R
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if not isinstalled('AMR'):
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utils = importr('utils')
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utils.install_packages('AMR')
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utils.install_packages('AMR', repos='https://msberends.r-universe.dev')
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# Python package version of AMR
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python_amr_version = metadata.version('AMR')
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# R package version of AMR
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# r_amr_version = robjects.r('packageVersion("AMR")')[0]
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# Compare R and Python package versions
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# if r_amr_version != python_amr_version:
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# print(f"{BLUE}AMR:{RESET} Version mismatch detected. Updating AMR R package version to {python_amr_version}...", flush=True)
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# try:
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# # Re-install the specific version of AMR in R
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# utils = importr('utils')
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# utils.install_packages('AMR', repos='https://msberends.r-universe.dev')
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# except Exception as e:
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# print(f"{BLUE}AMR:{RESET} Could not update: {e}{RESET}", flush=True)
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# Activate the automatic conversion between R and pandas DataFrames
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pandas2ri.activate()
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# example_isolates
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example_isolates = pandas2ri.rpy2py(robjects.r('''
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df <- AMR::example_isolates
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@ -68,6 +85,7 @@ df[] <- lapply(df, function(x) {
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x
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}
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})
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df <- df[, !sapply(df, is.list)]
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df
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'''))
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example_isolates['date'] = pd.to_datetime(example_isolates['date'])
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@ -75,7 +93,7 @@ example_isolates['date'] = pd.to_datetime(example_isolates['date'])
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# microorganisms
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microorganisms = pandas2ri.rpy2py(robjects.r('AMR::microorganisms[, !sapply(AMR::microorganisms, is.list)]'))
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antibiotics = pandas2ri.rpy2py(robjects.r('AMR::antibiotics[, !sapply(AMR::antibiotics, is.list)]'))
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clinical_breakpoints = pandas2ri.rpy2py(robjects.r('AMR::clinical_breakpoints'))
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clinical_breakpoints = pandas2ri.rpy2py(robjects.r('AMR::clinical_breakpoints[, !sapply(AMR::clinical_breakpoints, is.list)]'))
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print(f"{BLUE}AMR:{RESET} {GREEN}Done.{RESET}", flush=True)
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EOL
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@ -220,6 +238,7 @@ echo "Python wrapper functions generated in $functions_file."
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echo "Python wrapper functions listed in $init_file."
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cp ../vignettes/AMR_for_Python.Rmd python_wrapper/AMR/README.md
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sed -i '1,/^# Introduction$/d' python_wrapper/AMR/README.md
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echo "README copied"
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@ -24,17 +24,42 @@ knitr::opts_chunk$set(
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# Introduction
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The `AMR` package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the `AMR` Python package, which uses `rpy2` internally. This package allows Python users to access all the functions from the R `AMR` package without the need to set up `rpy2` themselves. Since this Python package is not a true 'port' (which would require all R functions to be rewritten into Python), R and the AMR R package are still required to be installed. Yet, Python users can now easily work with AMR data directly through Python code.
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The `AMR` package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the [`AMR` Python Package Index](https://pypi.org/project/AMR/).
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In this document, we explain how this works and provide simple examples of using the `AMR` Python package.
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This Python package is a wrapper round the `AMR` R package. It uses the `rpy2` package internally. Despite the need to have R installed, Python users can now easily work with AMR data directly through Python code.
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## How It Works
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# Install
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The `AMR` Python package acts as a wrapper around the functions in the `AMR` R package. The package simplifies the process of calling R functions in Python, eliminating the need to manually manage the `rpy2` setup, which Python uses internally to be able to work with the R package. By just using `import AMR`, Python users can directly use the functions from the `AMR` R package as if they were native Python functions.
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1. First make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
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Internally, `rpy2` is still being used, but all complexity is hidden from the user. This approach keeps the Python code clean and Pythonic, while still leveraging the full power of the R `AMR` package.
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For Linux:
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## Example of Usage
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```bash
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# Ubuntu / Debian
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sudo apt install r-base
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# Fedora:
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sudo dnf install R
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# CentOS/RHEL
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sudo yum install R
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```
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For macOS (using [Homebrew](https://brew.sh)):
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```bash
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brew install r
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```
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For Windows, visit the [CRAN download page](https://cran.r-project.org) to download and install R.
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2. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
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```bash
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pip install AMR
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```
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# Examples of Usage
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## Cleaning Taxonomy
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Here’s an example that demonstrates how to clean microorganism and drug names using the `AMR` Python package:
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@ -70,7 +95,8 @@ print(df)
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* **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
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### Taxonomic Data Sets Now in Python!
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## Taxonomic Data Sets Now in Python!
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As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
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@ -111,42 +137,7 @@ AMR.antibiotics
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| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
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# Installation
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To be able to use the `AMR` Python package, it is required to install both R and the `AMR` R package.
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### Preparation: Install R and `AMR` R package
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For Linux and macOS, this is just:
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```bash
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# Ubuntu / Debian
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sudo apt install r-base && Rscript -e 'install.packages("AMR")'
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# Fedora:
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sudo dnf install R && Rscript -e 'install.packages("AMR")'
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# CentOS/RHEL
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sudo yum install R && Rscript -e 'install.packages("AMR")'
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# Arch Linux
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sudo pacman -S r && Rscript -e 'install.packages("AMR")'
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# macOS
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brew install r && Rscript -e 'install.packages("AMR")'
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```
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For Windows, visit the [CRAN download page](https://cran.r-project.org) in install R, then afterwards install the 'AMR' package manually.
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### Install `AMR` Python Package
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Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
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```bash
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pip install AMR
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```
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# Working with `AMR` in Python
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Now that we have everything set up, let’s walk through some practical examples of using the `AMR` package within Python.
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## Example 1: Calculating AMR
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## Calculating AMR
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```python
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import AMR
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@ -161,7 +152,7 @@ print(result)
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[0.59555556]
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```
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## Example 2: Generating Antibiograms
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## Generating Antibiograms
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One of the core functions of the `AMR` package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python:
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@ -200,4 +191,6 @@ In this example, we generate an antibiogram by selecting various antibiotics.
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With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.
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By using `import AMR`, you can seamlessly integrate the robust features of the R `AMR` package into your Python workflows. Whether you're cleaning data or analysing resistance patterns, the `AMR` Python package makes it easy to work with AMR data in Python.
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By just running `import AMR`, users can seamlessly integrate the robust features of the R `AMR` package into Python workflows.
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Whether you're cleaning data or analysing resistance patterns, the `AMR` Python package makes it easy to work with AMR data in Python.
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